04_TNBC_2018_Spiat
2023-01-08
Libaries
# BiocManager::install("SPIAT")
library(SPIAT)
library(dplyr)
#library(ggspavis)
library(ggplot2)
library(SpatialExperiment)# colour_pal <- c(
# "#6600CC",
# "#CC0066",
# "#CC0066",
# "#FF0000",
# "#FFFF00",
# "#C65911",
# "#66FF33",
# "#66FF99",
# "#0070C0",
# "#92D050",
# "#66FFFF",
# "#2F02BE",
# "purple",
# "brown",
# "green",
# "grey"
# )Identifying Phenotype compartment
immune_names <- c("B", "CD3 T", "CD4 T", "CD8 T", "DC", "DC/Mono",
"T reg", "NK", "Mono/Neu", "Neu", "Mac", "Other immune")
nonimmune_names <- c("Endothelial", "Epithelial", "Mesenchymal")
tumor_names <- c("Other")Load RDS file of SPE
Load the spatial experiment object created earlier.
load(here::here("Output", "Data", "03_SPE", "03_TNBC_2018_spe.rds"))Check the SpatialExperiment object.
#spe
colnames(spe) <- paste0("Cell_", 1:dim(assay(spe))[[2]])
spe## class: SpatialExperiment
## dim: 36 179194
## metadata(0):
## assays(1): exprs
## rownames(36): betaCatenin CD11b ... SMA Vimentin
## rowData names(0):
## colnames(179194): Cell_1 Cell_2 ... Cell_179193 Cell_179194
## colData names(26): sample_id patient_id ... Si Ta
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(2) : centroidX centroidY
## imgData names(1): sample_idDefine color schemes for each phenotype
We want to define color for each phenotype, and keeping the color consistent in all samples.
color_vectors <- list()
phenotype_col <- setNames(object = c(
"#6600CC",
"#CC0066",
"#CC0066",
"#FF0000",
"#FFFF00",
"#C65911",
"#66FF33",
"#66FF99",
"#0070C0",
"#92D050",
"#66FFFF",
"#2F02BE",
"purple",
"brown",
"green",
"grey"), unique(spe$mm))
phenotype_col## B CD3 T CD4 T CD8 T DC DC/Mono
## "#6600CC" "#CC0066" "#CC0066" "#FF0000" "#FFFF00" "#C65911"
## Endothelial Epithelial Mac Mesenchymal Mono/Neu Neu
## "#66FF33" "#66FF99" "#0070C0" "#92D050" "#66FFFF" "#2F02BE"
## NK Other Other immune T reg
## "purple" "brown" "green" "grey"color_vectors$phenotype <- phenotype_col# phenotype_col <- data.frame(Type = unique(spe$mm),
# Colour = c(
# "#6600CC",
# "#CC0066",
# "#CC0066",
# "#FF0000",
# "#FFFF00",
# "#C65911",
# "#66FF33",
# "#66FF99",
# "#0070C0",
# "#92D050",
# "#66FFFF",
# "#2F02BE",
# "purple",
# "brown",
# "green",
# "grey"))Subsetting
Subset spe with sampel 1.
spe_1 <- spe[, spe$sample_id == "Sample_04"]
#celltype <- tibble(spe_1$mm)
#coordX <- tibble(spatialCoords(spe_1)[, "centroidX"])
#coordY <- tibble(spatialCoords(spe_1)[, "centroidY"])
celltype <- spe_1$mm
coordX <- spatialCoords(spe_1)[, "centroidX"]
coordY <- spatialCoords(spe_1)[, "centroidY"]Transform spe into SPIAT format
We want to transform the colData into the SPIAT format to further analysis with SPIAT packages.
spiat_sample1 <- SPIAT::format_image_to_spe(format = "general",
intensity_matrix = assay(spe_1),
phenotypes = celltype,
coord_x = coordX,
coord_y = coordY)
spiat_sample1## class: SpatialExperiment
## dim: 36 5381
## metadata(0):
## assays(1): ''
## rownames(36): betaCatenin CD11b ... SMA Vimentin
## rowData names(0):
## colnames(5381): Cell_1405 Cell_1406 ... Cell_178421 Cell_178422
## colData names(3): Cell.ID Phenotype sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(2) : Cell.X.Position Cell.Y.Position
## imgData names(0):Penotype compartment in the tissue
unique_cells <- unique(spiat_sample1$Phenotype)
immune_cells <- base::subset(unique_cells, unique_cells %in% immune_names)
nonimmune_cells <- base::subset(unique_cells, unique_cells %in% nonimmune_names)
tumor_cells <- base:: subset(unique_cells, unique_cells %in% tumor_names)Colour panel for each tissue
We need to customize colour panel for each tissue as they contain different number of unique phenotype (for SPIAT plotting purpose).
colour_pal <- phenotype_col[unique_cells]
colour_pal## B CD3 T CD4 T CD8 T DC DC/Mono
## "#6600CC" "#CC0066" "#CC0066" "#FF0000" "#FFFF00" "#C65911"
## Endothelial Epithelial Mac Mesenchymal Mono/Neu Neu
## "#66FF33" "#66FF99" "#0070C0" "#92D050" "#66FFFF" "#2F02BE"
## NK Other Other immune T reg
## "purple" "brown" "green" "grey"Visulaizing tissues
We want to visualize the phenotype in each tissues using catergorical dot plot.
p1 <- plot_cell_categories(spe_object = spiat_sample1,
categories_of_interest = unique(spiat_sample1$Phenotype),
colour_vector = colour_pal,
feature_colname = "Phenotype") +
#scale_color_manual(values = metadata(spiat_sample1)$color_vectors$phenotype) +
ggtitle("")
p1
Basic Analysis
We want to performing some basic analyses on the image.
Cell precentage
We obtain the number and proportion of each cell type and visualize them.
p_cells <- calculate_cell_proportions(spe_object = spiat_sample1,
reference_celltypes = NULL,
celltypes_to_exclude = NULL,
feature_colname = "Phenotype",
plot.image = TRUE)
p_cells## Cell_type Number_of_celltype Proportion Percentage Proportion_name
## 14 Other 1586 0.2947407545 29.47407545 /Total
## 10 Mesenchymal 676 0.1256272068 12.56272068 /Total
## 8 Epithelial 599 0.1113175990 11.13175990 /Total
## 3 CD4 T 545 0.1012822895 10.12822895 /Total
## 1 B 515 0.0957071176 9.57071176 /Total
## 15 Other immune 477 0.0886452332 8.86452332 /Total
## 4 CD8 T 279 0.0518490987 5.18490987 /Total
## 5 DC 223 0.0414421111 4.14421111 /Total
## 9 Mac 196 0.0364244564 3.64244564 /Total
## 16 T reg 86 0.0159821594 1.59821594 /Total
## 6 DC/Mono 78 0.0144954469 1.44954469 /Total
## 11 Mono/Neu 61 0.0113361829 1.13361829 /Total
## 12 Neu 40 0.0074335625 0.74335625 /Total
## 2 CD3 T 12 0.0022300688 0.22300688 /Total
## 13 NK 4 0.0007433563 0.07433563 /Total
## 7 Endothelial 4 0.0007433563 0.07433563 /TotalWe also want to plot the cell proportion as bar plots.
plot_cell_percentages(cell_proportions = p_cells,
cells_to_exclude = NULL,
cellprop_colname = "Proportion_name")
We can separte them as different compartment such as immune, non immune,
and other…
However, these plots seem to be a little bit chaos. We would like them be visualized in their comartment.
Immune Compartment
Cell percentage
p_immune <- calculate_cell_proportions(spe_object = spiat_sample1,
reference_celltypes = NULL,
celltypes_to_exclude = c(nonimmune_cells, tumor_cells),
feature_colname = "Phenotype",
plot.image = TRUE)
p_immune## Cell_type Number_of_celltype Proportion Percentage Proportion_name
## 3 CD4 T 545 0.267287886 26.7287886 /Total
## 1 B 515 0.252574792 25.2574792 /Total
## 4 CD8 T 279 0.136831780 13.6831780 /Total
## 5 DC 223 0.109367337 10.9367337 /Total
## 9 Mac 196 0.096125552 9.6125552 /Total
## 16 T reg 86 0.042177538 4.2177538 /Total
## 6 DC/Mono 78 0.038254046 3.8254046 /Total
## 11 Mono/Neu 61 0.029916626 2.9916626 /Total
## 12 Neu 40 0.019617460 1.9617460 /Total
## 2 CD3 T 12 0.005885238 0.5885238 /Total
## 13 NK 4 0.001961746 0.1961746 /TotalCell proportion
plot_cell_percentages(cell_proportions = p_immune,
cells_to_exclude = NULL,
cellprop_colname = "Proportion_name")
Non-Immune Compartment
Cell percentage
p_nonimmune <- calculate_cell_proportions(spe_object = spiat_sample1,
reference_celltypes = NULL,
celltypes_to_exclude = c(immune_cells, tumor_cells),
feature_colname = "Phenotype",
plot.image = TRUE)
p_nonimmune## Cell_type Number_of_celltype Proportion Percentage Proportion_name
## 10 Mesenchymal 676 0.528537920 52.8537920 /Total
## 8 Epithelial 599 0.468334636 46.8334636 /Total
## 7 Endothelial 4 0.003127443 0.3127443 /TotalCell proportion barplot
plot_cell_percentages(cell_proportions = p_nonimmune,
cells_to_exclude = NULL,
cellprop_colname = "Proportion_name")
Cell distance
We want to calculate the pairwise distances between two cell types(cell type A and cell type B) using violet plot.
distance <- calculate_pairwise_distances_between_celltypes(
spe_object = spiat_sample1,
cell_types_of_interest = c(nonimmune_cells, tumor_cells),
feature_colname = "Phenotype")plot_cell_distances_violin(distance)
We also calculate the summary statistics for the distance between each combinations of cell types.
summary_distance <- calculate_summary_distances_between_celltypes(distance)
summary_distance## Pair Mean Min Max Median Std.Dev
## 1 Endothelial/Endothelial 633.7053 300.95445 1143.275 599.9288 285.0360
## 2 Endothelial/Epithelial 1087.2253 249.25493 2314.492 1010.5140 352.8898
## 3 Endothelial/Mesenchymal 825.6341 14.65962 2342.063 780.7198 452.6015
## 4 Endothelial/Other 1023.3882 14.85366 2406.227 966.0057 427.0792
## 5 Epithelial/Endothelial 1087.2253 249.25493 2314.492 1010.5140 352.8898
## 6 Epithelial/Epithelial 762.7003 12.00658 2454.521 632.9717 523.0041
## 7 Epithelial/Mesenchymal 1147.0954 15.63464 2729.909 1124.8106 423.4883
## 8 Epithelial/Other 1166.3782 12.63314 2624.434 1222.7692 563.8142
## 9 Mesenchymal/Endothelial 825.6341 14.65962 2342.063 780.7198 452.6015
## 10 Mesenchymal/Epithelial 1147.0954 15.63464 2729.909 1124.8106 423.4883
## 11 Mesenchymal/Mesenchymal 838.7467 10.63704 2759.031 784.1352 456.7852
## 12 Mesenchymal/Other 1012.3537 11.59441 2816.976 982.7881 446.3273
## 13 Other/Endothelial 1023.3882 14.85366 2406.227 966.0057 427.0792
## 14 Other/Epithelial 1166.3782 12.63314 2624.434 1222.7692 563.8142
## 15 Other/Mesenchymal 1012.3537 11.59441 2816.976 982.7881 446.3273
## 16 Other/Other 1105.8901 12.14511 2659.581 1139.9157 544.8262
## Reference Target
## 1 Endothelial Endothelial
## 2 Endothelial Epithelial
## 3 Endothelial Mesenchymal
## 4 Endothelial Other
## 5 Epithelial Endothelial
## 6 Epithelial Epithelial
## 7 Epithelial Mesenchymal
## 8 Epithelial Other
## 9 Mesenchymal Endothelial
## 10 Mesenchymal Epithelial
## 11 Mesenchymal Mesenchymal
## 12 Mesenchymal Other
## 13 Other Endothelial
## 14 Other Epithelial
## 15 Other Mesenchymal
## 16 Other OtherInterpretaion of the above summry would be “the average pairwise distance between cells of B and cells of CD4 T are 642.958”.
plot_distance_heatmap(phenotype_distances_result = summary_distance,
metric = "mean")
Minimum cell distance
Different than the pairwise distance where it calculates all cell types of interest, here only identify distance to the cloest cell of type B to each of the cell of type A.
min_dist <- calculate_minimum_distances_between_celltypes(
spe_object = spiat_sample1,
cell_types_of_interest = c(nonimmune_cells, tumor_cells),
feature_colname = "Phenotype")## [1] "Markers had been selected in minimum distance calculation: "
## [1] "Endothelial" "Epithelial" "Mesenchymal" "Other"Visualize them with violin plot
plot_cell_distances_violin(min_dist)
sum_min_dist <- calculate_summary_distances_between_celltypes(min_dist)
sum_min_dist## Pair Mean Min Max Median Std.Dev
## 1 Endothelial/Epithelial 352.14926 249.25493 469.94212 344.69999 90.566481
## 2 Endothelial/Mesenchymal 16.13187 14.65962 16.92138 16.47324 1.055473
## 3 Endothelial/Other 36.90058 14.85366 51.75084 40.49891 15.721087
## 4 Epithelial/Endothelial 765.94661 249.25493 1311.36688 766.82740 153.315108
## 5 Epithelial/Mesenchymal 146.31005 15.63464 386.67559 140.02586 76.674329
## 6 Epithelial/Other 34.10005 12.63314 100.40677 29.97670 14.952184
## 7 Mesenchymal/Endothelial 494.45818 14.65962 1340.74121 393.29165 326.746983
## 8 Mesenchymal/Epithelial 316.83685 15.63464 749.59241 297.03752 185.970618
## 9 Mesenchymal/Other 45.15832 11.59441 282.57175 39.24468 26.367324
## 10 Other/Endothelial 698.72427 14.85366 1354.43391 704.14687 270.074215
## 11 Other/Epithelial 133.31777 12.63314 699.26277 64.75419 153.339697
## 12 Other/Mesenchymal 79.74269 11.59441 399.59002 62.66061 58.480358
## Reference Target
## 1 Endothelial Epithelial
## 2 Endothelial Mesenchymal
## 3 Endothelial Other
## 4 Epithelial Endothelial
## 5 Epithelial Mesenchymal
## 6 Epithelial Other
## 7 Mesenchymal Endothelial
## 8 Mesenchymal Epithelial
## 9 Mesenchymal Other
## 10 Other Endothelial
## 11 Other Epithelial
## 12 Other MesenchymalInterpretaion of the above summry would be “the average minimum distance between cells of B and cells of CD4 T are 89.539”.
Plot heatmap.
plot_distance_heatmap(phenotype_distances_result = sum_min_dist,
metric = "mean")
Cell colocalisation
We also want to qualify cell colocalisation, where it refers to how much two cell types are colocalisating and thus potentially interacting. Cell colocalisation metrics allow capturing a dominant spatial pattern in an image.
Cells in Neibourhood
Here, we calculate the avereage percentage of cells of one target cell type with a radius of a reference cell type.
average_percentage_of_cells_within_radius(spe_object = spiat_sample1,
target_celltype = "CD4 T",
reference_celltype = "B",
feature_colname = "Phenotype")## [1] 16.96122We can also calculate the average intensity of the target_marker within a radius from the cell positive for the reference marker. (Note that it pools all cells with the target marker that are within the specific radius of any reference cell.)
average_marker_intensity_within_radius(spe_object = spiat_sample1,
target_marker = "CD4",
reference_marker = "CD8",
radius = 30)## [1] 0.5157241We can also plot a line graph showing the intensity levels at each specified radius.
plot_average_intensity(spe_object = spiat_sample1,
target_marker = "CD4",
reference_marker = "CD8",
radii = c(15, 25, 35, 45, 55, 70, 85, 100))
Mixing Score and Normalised Mixing Score
The score was originally defined as the number of immune-tumor interactions divided by the number of immune-immune interactions from Keren et al. in 2018. - This mixing score is defined as the number of target-reference interactions/number of reference-reference interactions within a specified radius. The higher the score the greater the mixing of the two cell types. - The normalised score is normalised for the number of target and reference cells in the image.
mixing_score_summary(spe_object = spiat_sample1,
target_celltype = "B",
reference_celltype = "CD4 T",
feature_colname = "Phenotype",
radius = 20)## Reference Target Number_of_reference_cells Number_of_target_cells
## 2 CD4 T B 545 515
## Reference_target_interaction Reference_reference_interaction Mixing_score
## 2 108 274 0.3941606
## Normalised_mixing_score
## 2 0.8327121Cross K functions
Cross K function calculates the number of target cell types across a range of radii from a reference cell type, and compares the behaviour of the input image with an image of randomly distributed points using a Poisson point process. There are four patterns,
- The red line and black line are close to each other, meaning the two types of points are randomly independently distributed.
- The red line is under the black line, with a large difference in the middle of the plot, meaning the points are mixed and split into clusters.
- With the increase of radius, the black line diverges further from the red line, meaning that there is one mixed cluster of two types of points.
- The red line is above the black line, meaning that the two types of points form separated clusters.
df_cross <- calculate_cross_functions(spe_object = spiat_sample1,
method = "Kcross",
cell_types_of_interest = c("B", "CD4 T"),
feature_colname = "Phenotype",
dist = 100)
The above pattern is similar to the first patteren listed, which means that cell type B and cell type CD4 T are randonmly independently distributed.
We also want to calculate the area under the curve(AUC) of the cross-K function, to identidy the two cell types of cells whether:
- negative values: separate clusters
- positive values: mixing of cell types
AUC_of_cross_function(df.cross = df_cross)## [1] 0.1703536Thus, the AUC score is close to zero that the two types of cells either do not have a relationship or they form a ring surrounding a cluster.
Cross-K interaction
Details of such methodologies please see SPIAT vignettes.
crossing_of_crossK(df.cross = df_cross)## [1] "Crossing of cross K function is detected for this image, indicating a potential immune ring."
## [1] "The crossing happens at the 18% of the specified distance."## [1] 0.82The result shows that the crossing happens at 80% of the specified distance (100) of the cross K function, which is very close to the edge of the tumour cluster. This means that the crossing is not due to the randomness in cell distribution, nor due to two close located immune and tumour clusters. This result aligns with the observation that there is an immune ring surrounding the tumour cluster.
Spatial heterogeneity
We want to measure the spatial heterogeneity of patterns as dominant spatial patterns are unlikely to be distributed evenly in a tissue.
Localised Entropy
Entropy in spatial analysis refers to the balance in the number of cells of distinct populations. An entropy score can be obtained for an entire image, but does not provide any information of the image.
calculate_entropy(spe_object = spiat_sample1,
cell_types_of_interest = unique(spiat_sample1$Phenotype),
feature_colname = "Phenotype")## [1] 3.147107Fishnet Grid
One approach to calculate localised metric is to split the image into fishnet grid squares. For each grid square, grid_metrics() calculates the metric for that square and visualise the raster image. Users can choose any metric as the localised metric.
grid <- grid_metrics(spe_object = spiat_sample1,
FUN = calculate_entropy,
cell_types_of_interest = unique(spiat_sample1$Phenotype),
feature_colname = "Phenotype",
n_split = 20)
Characterize Tissue Structure
We want to focus on understanding the spatial distribution of an certain cell type of cell populations realtive to tissue regions. We will perform a analysis to characterize the immune population in tumor structure that can generalised to other tissue and cell types.
Determine if not a clear tumor margin
The following function quantify the quality of the ‘margin’ by calculate the ratio of tumor bordering cells to tumour cells(R-BT). This ratio is high when there is a disproportional high number of tumour margin cells compared to internal tumour cells.
R_BC(spe_object = spiat_sample1,
cell_type_of_interest = "Other",
feature_colname = "Phenotype")
## [1] 0.2629256The result is 0.10899. This low value means there are relatively low number of bordering cells compared to total tumour cells, meaning that this image has clear tumour margins.
Automatic identification of the tumor margin
We now want to identify the bordering cells.
formatted_border <- identify_bordering_cells(spe_object = spiat_sample1,
reference_cell = "Other",
feature_colname = "Phenotype")## [1] "The alpha of Polygon is: 68.0375"
We now want to get the number of cell type clusters
attr(formatted_border, "n_of_clusters")## [1] 4There are 2 B cell clusters in the image.
Classfication of cells based on locations
We want to define our locations relative to the margin based on distances:
- Internal Margin: bordering the margin, inside the tumour area
- External Margin: bordering the margin, surrounding the tumour area
- Inside: Inside the tumor area
- Outside: Outside the tumor area
We now calculate the distance of cells to the tumor margin.
formatted_distance <- calculate_distance_to_margin(formatted_border)## [1] "Markers had been selected in minimum distance calculation: "
## [1] "Non-border" "Border"Next, we classify cell based on their location. As a distance cutoff, we use a distance of 5 cells from the tumour margin. The function first calculates the average minimum distance between all pairs of nearest cells and then multiples this number by 5.
immune_cells <- immune_names[unique(spiat_sample1$Phenotype) %in% immune_names]
immune_cells## [1] "B" "CD3 T" "CD4 T" "CD8 T" "DC"
## [6] "DC/Mono" "Mono/Neu" "Mac" "Other immune" NA
## [11] NA NAformatted_structure <- define_structure(spe_object = formatted_distance,
cell_types_of_interest = immune_cells,
feature_colname = "Phenotype",
n_margin_layers = 5)
categories <- unique(formatted_structure$Structure)We now plot and color these structure categories.
plot_cell_categories(spe_object = formatted_structure,
feature_colname = "Structure")
We also calculate the proportions of immune cells in each of the locations.
immune_proportions <- calculate_proportions_of_cells_in_structure(spe_object = formatted_structure,
cell_types_of_interest = immune_names,
feature_colname = "Phenotype")
immune_proportions## Cell.Type Relative_to
## 1 B All_cells_in_the_structure
## 2 CD3 T All_cells_in_the_structure
## 3 CD4 T All_cells_in_the_structure
## 4 CD8 T All_cells_in_the_structure
## 5 DC All_cells_in_the_structure
## 6 DC/Mono All_cells_in_the_structure
## 7 T reg All_cells_in_the_structure
## 8 NK All_cells_in_the_structure
## 9 Mono/Neu All_cells_in_the_structure
## 10 Neu All_cells_in_the_structure
## 11 Mac All_cells_in_the_structure
## 12 Other immune All_cells_in_the_structure
## 13 B All_cells_of_interest_in_the_structure
## 14 CD3 T All_cells_of_interest_in_the_structure
## 15 CD4 T All_cells_of_interest_in_the_structure
## 16 CD8 T All_cells_of_interest_in_the_structure
## 17 DC All_cells_of_interest_in_the_structure
## 18 DC/Mono All_cells_of_interest_in_the_structure
## 19 T reg All_cells_of_interest_in_the_structure
## 20 NK All_cells_of_interest_in_the_structure
## 21 Mono/Neu All_cells_of_interest_in_the_structure
## 22 Neu All_cells_of_interest_in_the_structure
## 23 Mac All_cells_of_interest_in_the_structure
## 24 Other immune All_cells_of_interest_in_the_structure
## 25 B The_same_cell_type_in_the_whole_image
## 26 CD3 T The_same_cell_type_in_the_whole_image
## 27 CD4 T The_same_cell_type_in_the_whole_image
## 28 CD8 T The_same_cell_type_in_the_whole_image
## 29 DC The_same_cell_type_in_the_whole_image
## 30 DC/Mono The_same_cell_type_in_the_whole_image
## 31 T reg The_same_cell_type_in_the_whole_image
## 32 NK The_same_cell_type_in_the_whole_image
## 33 Mono/Neu The_same_cell_type_in_the_whole_image
## 34 Neu The_same_cell_type_in_the_whole_image
## 35 Mac The_same_cell_type_in_the_whole_image
## 36 Other immune The_same_cell_type_in_the_whole_image
## 37 All_cells_of_interest All_cells_in_the_structure
## P.Infiltrated.CoI P.Internal.Margin.CoI P.External.Margin.CoI P.Stromal.CoI
## 1 0.001709402 0.033477991 0.108585859 0.224824356
## 2 0.000000000 0.000000000 0.003156566 0.005464481
## 3 0.003418803 0.021078735 0.131944444 0.234192037
## 4 0.011965812 0.028518289 0.080176768 0.077283372
## 5 0.001709402 0.010539368 0.069444444 0.074160812
## 6 0.000000000 0.003099814 0.020202020 0.032006245
## 7 0.000000000 0.000000000 0.000000000 0.000000000
## 8 0.000000000 0.000000000 0.000000000 0.000000000
## 9 0.010256410 0.007439554 0.014520202 0.015612802
## 10 0.000000000 0.000000000 0.000000000 0.000000000
## 11 0.000000000 0.016119033 0.051136364 0.069476971
## 12 0.010256410 0.056416615 0.148358586 0.113192818
## 13 0.043478261 0.189473684 0.173038229 0.265682657
## 14 0.000000000 0.000000000 0.005030181 0.006457565
## 15 0.086956522 0.119298246 0.210261569 0.276752768
## 16 0.304347826 0.161403509 0.127766600 0.091328413
## 17 0.043478261 0.059649123 0.110663984 0.087638376
## 18 0.000000000 0.017543860 0.032193159 0.037822878
## 19 0.000000000 0.000000000 0.000000000 0.000000000
## 20 0.000000000 0.000000000 0.000000000 0.000000000
## 21 0.260869565 0.042105263 0.023138833 0.018450185
## 22 0.000000000 0.000000000 0.000000000 0.000000000
## 23 0.000000000 0.091228070 0.081488934 0.082103321
## 24 0.260869565 0.319298246 0.236418511 0.133763838
## 25 0.001941748 0.104854369 0.333980583 0.559223301
## 26 0.000000000 0.000000000 0.416666667 0.583333333
## 27 0.003669725 0.062385321 0.383486239 0.550458716
## 28 0.025089606 0.164874552 0.455197133 0.354838710
## 29 0.004484305 0.076233184 0.493273543 0.426008969
## 30 0.000000000 0.064102564 0.410256410 0.525641026
## 31 0.000000000 0.000000000 0.000000000 0.000000000
## 32 0.000000000 0.000000000 0.000000000 0.000000000
## 33 0.098360656 0.196721311 0.377049180 0.327868852
## 34 0.000000000 0.000000000 0.000000000 0.000000000
## 35 0.000000000 0.132653061 0.413265306 0.454081633
## 36 0.012578616 0.190775681 0.492662474 0.303983229
## 37 0.039316239 0.176689399 0.627525253 0.846213895Lastly, we calculate summaries of distance of immune cells in the tumor structure.
immune_distance <- calculate_summary_distances_of_cells_to_borders(spe_object = formatted_structure,
cell_types_of_interest = immune_names,
feature_colname = "Phenotype")
immune_distance## Cell.Type Area Min_d Max_d Mean_d
## 1 All_cell_types_of_interest Within_border_area 13.62429 230.11512 51.26510
## 2 All_cell_types_of_interest Stroma 14.08942 552.34256 146.39155
## 3 B Within_border_area 17.72247 122.72447 46.13370
## 4 B Stroma 14.89693 548.31367 172.66772
## 5 CD3 T Within_border_area NA NA NA
## 6 CD3 T Stroma 25.93917 437.86653 185.69582
## 7 CD4 T Within_border_area 13.62429 177.98893 44.77514
## 8 CD4 T Stroma 14.08942 552.34256 167.43030
## 9 CD8 T Within_border_area 17.59322 230.11512 54.96983
## 10 CD8 T Stroma 14.93929 456.97453 131.65782
## 11 DC Within_border_area 18.67978 115.19337 49.11441
## 12 DC Stroma 14.99489 516.26682 117.35931
## 13 DC/Mono Within_border_area 20.59185 98.43055 57.56269
## 14 DC/Mono Stroma 16.76060 461.88757 163.13484
## 15 T reg Within_border_area 28.43179 139.50750 62.34038
## 16 T reg Stroma 17.67966 405.82424 111.99799
## 17 NK Within_border_area NA NA NA
## 18 NK Stroma 35.98391 338.43103 152.66999
## 19 Mono/Neu Within_border_area 20.26714 221.18443 76.39486
## 20 Mono/Neu Stroma 16.14103 485.11780 113.13285
## 21 Neu Within_border_area 28.40728 164.83390 92.97730
## 22 Neu Stroma 25.04924 459.37861 157.23073
## 23 Mac Within_border_area 14.63485 84.40512 39.17638
## 24 Mac Stroma 16.62943 416.54969 138.59129
## 25 Other immune Within_border_area 15.55036 167.84719 46.25015
## 26 Other immune Stroma 16.46051 525.12593 119.48438
## Median_d St.dev_d
## 1 40.84218 36.30870
## 2 106.60417 114.19024
## 3 43.57616 22.60391
## 4 135.61806 123.65639
## 5 NA NA
## 6 218.38207 134.10566
## 7 37.24807 36.18568
## 8 131.35823 123.75076
## 9 38.41117 45.77380
## 10 86.92854 105.41112
## 11 47.01128 25.73717
## 12 95.74461 88.89667
## 13 42.86703 37.69157
## 14 116.86031 125.99122
## 15 53.58508 31.90796
## 16 81.36862 93.51937
## 17 NA NA
## 18 118.13252 134.51916
## 19 51.36990 57.46312
## 20 81.80619 91.49616
## 21 88.35051 47.12003
## 22 158.76148 130.63778
## 23 35.94441 18.55930
## 24 106.11399 101.59468
## 25 36.64403 30.63708
## 26 81.43001 99.34922Celluar neighbourhood
A celluar neighbourhood is defined as a group of cells that clusters together, where it can be cells of a single class and it can be a cells of mixture of cell types.
We want to identifies the celluar neighbourhood, where there are three methods of Hierarchical Clustering, dbsacan, and phenograph.
To move forward with hierarchical clustering algorithm, we would first compute the average minimum ditance between cells in the tissue.
average_minimum_distance(spe_object = spiat_sample1)## [1] 20.61341We then identify the neighbourhood with a radius of 50, and with a minimum neighbourhood size of 100.
clusters <- identify_neighborhoods(spe_object = spiat_sample1,
method = "hierarchical",
cell_types_of_interest = immune_names,
radius = 50,
min_neighborhood_size = 20,
feature_colname = "Phenotype",
no_pheno = NULL)
We can also visualize the cell composition of the neighbourhood by obtaining the percentages of cells with a specific phenotype within each neighbourhood and the number of cells in the neighbourhood.
neighborhoods_vis <- composition_of_neighborhoods(spe_object = clusters, feature_colname = "Phenotype")
neighborhoods_vis <- neighborhoods_vis[neighborhoods_vis$Total_number_of_cells >= 5, ]We then plot the composition of the neighbourhood and show as a heatmap.
plot_composition_heatmap(composition = neighborhoods_vis,
feature_colname = "Phenotype")
Average Nearest Neighbourhood Index
We can use ANNI to test the presence of neighbourhood, output whether there is a clear neighbourhood (clustered) or unclear neighbourhood (random/dispersed).
average_nearest_neighbor_index(spe_object = clusters,
reference_celltypes = c("Cluster_4"),
feature_colname = "Neighborhood", p_val = 0.05)## $ANN_index
## [1] 0.7688743
##
## $pattern
## [1] "Clustered"
##
## $`p-value`
## [1] 2.739314e-12average_nearest_neighbor_index(spe_object = clusters,
reference_celltypes = immune_names,
feature_colname = "Phenotype", p_val = 0.05)## $ANN_index
## [1] 1.198183
##
## $pattern
## [1] "Dispersed"
##
## $`p-value`
## [1] 6.11338e-81